
"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					lowercase__(					lowerCamelCase__ ):
	'''simple docstring'''

	def __init__(    self     :List[Any]		,			lowerCamelCase_     :List[str]		,			lowerCamelCase_     :str       )				->      List[Any]:







			'''simple docstring'''




			super().__init__()

			# make sure scheduler can always be converted to DDIM
			SCREAMING_SNAKE_CASE							:  Any					=			DDIMScheduler.from_config(scheduler.config       )

			self.register_modules(unet=__snake_case		,			scheduler=__snake_case       )







	@torch.no_grad()
	def __call__(    self     :Any		,			lowerCamelCase_     :int = 1		,			lowerCamelCase_     :Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowerCamelCase_     :float = 0.0		,			lowerCamelCase_     :int = 50		,			lowerCamelCase_     :Optional[bool] = None		,			lowerCamelCase_     :Optional[str] = "pil"		,			lowerCamelCase_     :bool = True		,			)				->      int:







			'''simple docstring'''




			if isinstance(self.unet.config.sample_size		,			__snake_case       ):
					SCREAMING_SNAKE_CASE							:  Dict					=			(
					    batch_size,
					    self.unet.config.in_channels,
					    self.unet.config.sample_size,
					    self.unet.config.sample_size,
					)
			else:
					SCREAMING_SNAKE_CASE							:  Optional[Any]					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

			if isinstance(__snake_case		,			__snake_case       ) and len(__snake_case       ) != batch_size:
					raise ValueError(
					    f"You have passed a list of generators of length {len(__snake_case       )}, but requested an effective batch"
					    f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

			SCREAMING_SNAKE_CASE							:  Dict					=			randn_tensor(__snake_case		,			generator=__snake_case		,			device=self.device		,			dtype=self.unet.dtype       )

			# set step values
			self.scheduler.set_timesteps(__snake_case       )

			for t in self.progress_bar(self.scheduler.timesteps       ):
					# 1. predict noise model_output
					SCREAMING_SNAKE_CASE							:  int					=			self.unet(__snake_case		,			__snake_case       ).sample

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					SCREAMING_SNAKE_CASE							:  Optional[int]					=			self.scheduler.step(
					    __snake_case		,			__snake_case		,			__snake_case		,			eta=__snake_case		,			use_clipped_model_output=__snake_case		,			generator=__snake_case       ).prev_sample

			SCREAMING_SNAKE_CASE							:  Dict					=			(image / 2 + 0.5).clamp(0		,			1       )
			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			image.cpu().permute(0		,			2		,			3		,			1       ).numpy()
			if output_type == "pil":
					SCREAMING_SNAKE_CASE							:  Optional[int]					=			self.numpy_to_pil(__snake_case       )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=__snake_case       )










'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """bert"""







    def __init__( self					:    List[str]		,     __snake_case					:    Optional[Any]=3_05_22		,     __snake_case					:    Any=7_68		,     __snake_case					:    str=12		,     __snake_case					:    Optional[int]=12		,     __snake_case					:    Tuple=30_72		,     __snake_case					:    Optional[Any]="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    Optional[int]=0.1		,     __snake_case					:    List[Any]=5_12		,     __snake_case					:    Optional[Any]=2		,     __snake_case					:    Optional[Any]=0.02		,     __snake_case					:    List[str]=1e-1_2		,     __snake_case					:    Dict=0		,     __snake_case					:    List[str]="absolute"		,     __snake_case					:    List[Any]=True		,     __snake_case					:    List[str]=None		,     **__snake_case					:    Any		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   List[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   List[Any]	  =	num_attention_heads
      a		:   str	  =	hidden_act
      a		:   str	  =	intermediate_size
      a		:   Tuple	  =	hidden_dropout_prob
      a		:   Union[str, Any]	  =	attention_probs_dropout_prob
      a		:   List[Any]	  =	max_position_embeddings
      a		:   Union[str, Any]	  =	type_vocab_size
      a		:   List[Any]	  =	initializer_range
      a		:   Dict	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   List[str]	  =	use_cache
      a		:   List[str]	  =	classifier_dropout

class 				a__(   lowerCamelCase__      ):







    @property
    def      lowercase_			( self					:    List[str]      ):
      if self.task == "multiple-choice":
        a		:   Tuple	  =	{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
        a		:   List[Any]	  =	{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ]      )